{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c948fc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (1000, 4)\n",
      "\n",
      "前5行数据:\n",
      "      电视    广播    报纸     收益\n",
      "0  230.1  37.8  69.2  331.5\n",
      "1   44.5  39.3  45.1  156.0\n",
      "2   17.2  45.9  69.3  139.5\n",
      "3  151.5  41.3  58.5  277.5\n",
      "4  180.8  10.8  58.4  193.5\n",
      "\n",
      "数据描述性统计:\n",
      "               电视           广播           报纸           收益\n",
      "count  1000.00000  1000.000000  1000.000000  1000.000000\n",
      "mean    149.16210    25.238900    32.553800   232.380000\n",
      "std      85.68557    14.966932    21.802912    79.006523\n",
      "min       0.70000     0.000000     0.300000    24.000000\n",
      "25%      75.10000    11.875000    14.775000   175.500000\n",
      "50%     151.50000    24.750000    28.200000   222.000000\n",
      "75%     221.95000    38.600000    46.125000   288.000000\n",
      "max     299.60000    53.900000   118.000000   450.000000\n",
      "\n",
      "特征变量: ['电视', '广播', '报纸']\n",
      "目标变量统计: 均值=232.38, 标准差=79.01, 范围=[24.00, 450.00]\n",
      "\n",
      "训练集形状: (800, 3), 测试集形状: (200, 3)\n",
      "\n",
      "======================================================================\n",
      "各回归模型性能评估\n",
      "======================================================================\n",
      "\n",
      "=== 线性回归 ===\n",
      "R²分数: 0.8404\n",
      "均方根误差(RMSE): 31.9367\n",
      "平均绝对误差(MAE): 24.7964\n",
      "\n",
      "=== 岭回归 ===\n",
      "R²分数: 0.8404\n",
      "均方根误差(RMSE): 31.9367\n",
      "平均绝对误差(MAE): 24.7964\n",
      "\n",
      "=== Lasso回归 ===\n",
      "R²分数: 0.8404\n",
      "均方根误差(RMSE): 31.9391\n",
      "平均绝对误差(MAE): 24.8008\n",
      "\n",
      "=== 弹性网络 ===\n",
      "R²分数: 0.8403\n",
      "均方根误差(RMSE): 31.9461\n",
      "平均绝对误差(MAE): 24.8108\n",
      "\n",
      "=== 决策树 ===\n",
      "R²分数: 0.9338\n",
      "均方根误差(RMSE): 20.5640\n",
      "平均绝对误差(MAE): 16.2975\n",
      "\n",
      "=== 随机森林 ===\n",
      "R²分数: 0.9567\n",
      "均方根误差(RMSE): 16.6249\n",
      "平均绝对误差(MAE): 13.7366\n",
      "\n",
      "=== 梯度提升树 ===\n",
      "R²分数: 0.9517\n",
      "均方根误差(RMSE): 17.5652\n",
      "平均绝对误差(MAE): 14.6476\n",
      "\n",
      "=== AdaBoost ===\n",
      "R²分数: 0.9349\n",
      "均方根误差(RMSE): 20.3921\n",
      "平均绝对误差(MAE): 16.9665\n",
      "\n",
      "=== SVM回归 ===\n",
      "R²分数: 0.6010\n",
      "均方根误差(RMSE): 50.4895\n",
      "平均绝对误差(MAE): 40.4148\n",
      "\n",
      "=== K近邻 ===\n",
      "R²分数: 0.9222\n",
      "均方根误差(RMSE): 22.2974\n",
      "平均绝对误差(MAE): 18.0195\n",
      "\n",
      "=== XGBoost ===\n",
      "R²分数: 0.9543\n",
      "均方根误差(RMSE): 17.0948\n",
      "平均绝对误差(MAE): 13.5189\n",
      "\n",
      "=== LightGBM ===\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000072 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 746\n",
      "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n",
      "[LightGBM] [Info] Start training from score 232.460625\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "R²分数: 0.9570\n",
      "均方根误差(RMSE): 16.5719\n",
      "平均绝对误差(MAE): 13.7004\n",
      "\n",
      "======================================================================\n",
      "模型性能比较汇总\n",
      "======================================================================\n",
      "          模型      R²分数       RMSE        MAE\n",
      "11  LightGBM  0.957020  16.571885  13.700402\n",
      "5       随机森林  0.956745  16.624852  13.736644\n",
      "10   XGBoost  0.954265  17.094774  13.518871\n",
      "6      梯度提升树  0.951714  17.565157  14.647578\n",
      "7   AdaBoost  0.934920  20.392130  16.966539\n",
      "4        决策树  0.933819  20.563955  16.297500\n",
      "9        K近邻  0.922191  22.297398  18.019500\n",
      "0       线性回归  0.840375  31.936708  24.796410\n",
      "1        岭回归  0.840375  31.936728  24.796441\n",
      "2    Lasso回归  0.840352  31.939071  24.800775\n",
      "3       弹性网络  0.840281  31.946147  24.810779\n",
      "8      SVM回归  0.601048  50.489483  40.414828\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_60280\\3391513921.py:171: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "D:\\pppython\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "特征重要性分析\n",
      "======================================================================\n",
      "线性回归系数分析:\n",
      "  特征名称        系数     系数绝对值\n",
      "1   广播  2.743047  2.743047\n",
      "0   电视  0.685291  0.685291\n",
      "2   报纸  0.011874  0.011874\n",
      "\n",
      "随机森林特征重要性:\n",
      "  特征名称       重要性\n",
      "0   电视  0.630166\n",
      "1   广播  0.351535\n",
      "2   报纸  0.018299\n",
      "\n",
      "梯度提升树特征重要性:\n",
      "  特征名称       重要性\n",
      "0   电视  0.638286\n",
      "1   广播  0.355142\n",
      "2   报纸  0.006572\n",
      "\n",
      "XGBoost特征重要性:\n",
      "  特征名称       重要性\n",
      "0   电视  0.567579\n",
      "1   广播  0.411087\n",
      "2   报纸  0.021335\n",
      "\n",
      "LightGBM特征重要性:\n",
      "  特征名称   重要性\n",
      "1   广播  1049\n",
      "2   报纸   963\n",
      "0   电视   950\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "最佳模型: LightGBM\n",
      "最佳R²分数: 0.9570\n",
      "======================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "前20个样本的详细预测结果:\n",
      "     实际收益    预测收益   绝对误差  相对误差(%)\n",
      "0   190.5  192.61   2.11     1.11\n",
      "1   292.5  295.12   2.62     0.90\n",
      "2   171.0  179.93   8.93     5.22\n",
      "3   324.0  293.46  30.54     9.43\n",
      "4   144.0  166.86  22.86    15.88\n",
      "5   366.0  349.49  16.51     4.51\n",
      "6   370.5  378.22   7.72     2.08\n",
      "7   166.5  140.11  26.39    15.85\n",
      "8   120.0  141.71  21.71    18.09\n",
      "9   139.5   86.17  53.33    38.23\n",
      "10  114.0  140.12  26.12    22.91\n",
      "11  202.5  203.88   1.38     0.68\n",
      "12  159.0  173.47  14.47     9.10\n",
      "13  207.0  195.10  11.90     5.75\n",
      "14  229.5  258.87  29.37    12.80\n",
      "15  147.0  143.73   3.27     2.23\n",
      "16  193.5  213.82  20.32    10.50\n",
      "17  262.5  240.27  22.23     8.47\n",
      "18  270.0  278.88   8.88     3.29\n",
      "19  247.5  237.14  10.36     4.19\n",
      "\n",
      "最佳模型(LightGBM)性能统计:\n",
      "平均绝对误差: 17.05\n",
      "最大绝对误差: 53.33\n",
      "平均相对误差: 9.56%\n",
      "\n",
      "======================================================================\n",
      "新数据预测示例\n",
      "======================================================================\n",
      "输入特征: [71, 11, 2]\n",
      "预测收益: 140.00\n",
      "\n",
      "======================================================================\n",
      "LightGBM参数调优\n",
      "======================================================================\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 746\n",
      "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n",
      "[LightGBM] [Info] Start training from score 232.460625\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "最优参数: {'learning_rate': 0.3, 'n_estimators': 50, 'num_leaves': 31}\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000040 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 746\n",
      "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n",
      "[LightGBM] [Info] Start training from score 232.460625\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "调优后LightGBM R²分数: 0.9559\n",
      "\n",
      "包含调优LightGBM的最终排名:\n",
      "              模型      R²分数       RMSE        MAE\n",
      "0       LightGBM  0.957020  16.571885  13.700402\n",
      "1           随机森林  0.956745  16.624852  13.736644\n",
      "12  LightGBM(调优)  0.955862  16.793618  13.780070\n",
      "2        XGBoost  0.954265  17.094774  13.518871\n",
      "3          梯度提升树  0.951714  17.565157  14.647578\n",
      "4       AdaBoost  0.934920  20.392130  16.966539\n",
      "5            决策树  0.933819  20.563955  16.297500\n",
      "6            K近邻  0.922191  22.297398  18.019500\n",
      "7           线性回归  0.840375  31.936708  24.796410\n",
      "8            岭回归  0.840375  31.936728  24.796441\n",
      "9        Lasso回归  0.840352  31.939071  24.800775\n",
      "10          弹性网络  0.840281  31.946147  24.810779\n",
      "11         SVM回归  0.601048  50.489483  40.414828\n",
      "\n",
      "🎯 最终推荐模型: LightGBM\n",
      "📊 最佳R²分数: 0.9570\n",
      "💡 建议: 在广告收益预测场景中，R² > 0.8 通常表示模型具有良好的预测能力\n",
      "\n",
      "======================================================================\n",
      "业务应用建议\n",
      "======================================================================\n",
      "\n",
      "1. 模型选择: 优先选择R²分数高且误差小的模型\n",
      "2. 特征重要性: 关注对收益预测最重要的广告特征\n",
      "3. 预测应用: 可用于广告投放策略优化和收益预估\n",
      "4. 监控更新: 定期重新训练模型以适应数据变化\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 设置中文字体支持\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 读取数据\n",
    "import pandas as pd\n",
    "df = pd.read_excel(\"D:\\贺之恒的python\\pppython\\python商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第10章 机器学习神器：XGBoost&LightGBM模型\\源代码汇总_Pycharm\\广告收益数据.xlsx\")\n",
    "print(\"数据形状:\", df.shape)\n",
    "print(\"\\n前5行数据:\")\n",
    "print(df.head())\n",
    "\n",
    "print(\"\\n数据描述性统计:\")\n",
    "print(df.describe())\n",
    "\n",
    "# 1.提取特征变量和目标变量\n",
    "X = df.drop(columns='收益') \n",
    "y = df['收益']  \n",
    "\n",
    "print(f\"\\n特征变量: {X.columns.tolist()}\")\n",
    "print(f\"目标变量统计: 均值={y.mean():.2f}, 标准差={y.std():.2f}, 范围=[{y.min():.2f}, {y.max():.2f}]\")\n",
    "\n",
    "# 2.划分训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
    "\n",
    "print(f\"\\n训练集形状: {X_train.shape}, 测试集形状: {X_test.shape}\")\n",
    "\n",
    "# 3.导入多种回归模型\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "import numpy as np\n",
    "\n",
    "# 检查是否安装了lightgbm和xgboost\n",
    "try:\n",
    "    from lightgbm import LGBMRegressor\n",
    "    LGBM_AVAILABLE = True\n",
    "except ImportError:\n",
    "    print(\"LightGBM未安装，跳过LightGBM模型\")\n",
    "    LGBM_AVAILABLE = False\n",
    "\n",
    "try:\n",
    "    from xgboost import XGBRegressor\n",
    "    XGB_AVAILABLE = True\n",
    "except ImportError:\n",
    "    print(\"XGBoost未安装，跳过XGBoost模型\")\n",
    "    XGB_AVAILABLE = False\n",
    "\n",
    "# 创建模型字典\n",
    "models = {\n",
    "    '线性回归': LinearRegression(),\n",
    "    '岭回归': Ridge(random_state=123),\n",
    "    'Lasso回归': Lasso(random_state=123),\n",
    "    '弹性网络': ElasticNet(random_state=123),\n",
    "    '决策树': DecisionTreeRegressor(random_state=123),\n",
    "    '随机森林': RandomForestRegressor(random_state=123),\n",
    "    '梯度提升树': GradientBoostingRegressor(random_state=123),\n",
    "    'AdaBoost': AdaBoostRegressor(random_state=123),\n",
    "    'SVM回归': SVR(),\n",
    "    'K近邻': KNeighborsRegressor()\n",
    "}\n",
    "\n",
    "# 添加可选的模型\n",
    "if XGB_AVAILABLE:\n",
    "    models['XGBoost'] = XGBRegressor(random_state=123)\n",
    "\n",
    "if LGBM_AVAILABLE:\n",
    "    models['LightGBM'] = LGBMRegressor(random_state=123)\n",
    "\n",
    "# 存储结果\n",
    "results = {}\n",
    "\n",
    "# 4.训练和评估各个模型\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"各回归模型性能评估\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    print(f\"\\n=== {model_name} ===\")\n",
    "    \n",
    "    # 模型训练\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # 模型预测\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # 评估指标\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    \n",
    "    print(f\"R²分数: {r2:.4f}\")\n",
    "    print(f\"均方根误差(RMSE): {rmse:.4f}\")\n",
    "    print(f\"平均绝对误差(MAE): {mae:.4f}\")\n",
    "    \n",
    "    # 存储结果\n",
    "    results[model_name] = {\n",
    "        'model': model,\n",
    "        'r2': r2,\n",
    "        'rmse': rmse,\n",
    "        'mae': mae,\n",
    "        'y_pred': y_pred\n",
    "    }\n",
    "\n",
    "# 5.比较模型性能\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"模型性能比较汇总\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "performance_df = pd.DataFrame({\n",
    "    '模型': list(results.keys()),\n",
    "    'R²分数': [result['r2'] for result in results.values()],\n",
    "    'RMSE': [result['rmse'] for result in results.values()],\n",
    "    'MAE': [result['mae'] for result in results.values()]\n",
    "}).sort_values('R²分数', ascending=False)\n",
    "\n",
    "print(performance_df)\n",
    "\n",
    "# 6.可视化模型性能比较\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "\n",
    "# R²分数比较\n",
    "bars1 = axes[0,0].bar(performance_df['模型'], performance_df['R²分数'], \n",
    "                     color=plt.cm.Set3(np.arange(len(performance_df))), alpha=0.8)\n",
    "axes[0,0].set_title('各模型R²分数比较', fontsize=14, fontweight='bold')\n",
    "axes[0,0].set_ylabel('R²分数', fontsize=12)\n",
    "axes[0,0].tick_params(axis='x', rotation=45)\n",
    "# 在柱状图上添加数值\n",
    "for bar in bars1:\n",
    "    height = bar.get_height()\n",
    "    axes[0,0].text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "                  f'{height:.3f}', ha='center', va='bottom', fontweight='bold')\n",
    "\n",
    "# RMSE比较\n",
    "bars2 = axes[0,1].bar(performance_df['模型'], performance_df['RMSE'], \n",
    "                     color=plt.cm.Pastel1(np.arange(len(performance_df))), alpha=0.8)\n",
    "axes[0,1].set_title('各模型RMSE比较', fontsize=14, fontweight='bold')\n",
    "axes[0,1].set_ylabel('RMSE', fontsize=12)\n",
    "axes[0,1].tick_params(axis='x', rotation=45)\n",
    "for bar in bars2:\n",
    "    height = bar.get_height()\n",
    "    axes[0,1].text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "                  f'{height:.3f}', ha='center', va='bottom', fontweight='bold')\n",
    "\n",
    "# MAE比较\n",
    "bars3 = axes[1,0].bar(performance_df['模型'], performance_df['MAE'], \n",
    "                     color=plt.cm.Set2(np.arange(len(performance_df))), alpha=0.8)\n",
    "axes[1,0].set_title('各模型MAE比较', fontsize=14, fontweight='bold')\n",
    "axes[1,0].set_ylabel('MAE', fontsize=12)\n",
    "axes[1,0].tick_params(axis='x', rotation=45)\n",
    "for bar in bars3:\n",
    "    height = bar.get_height()\n",
    "    axes[1,0].text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "                  f'{height:.3f}', ha='center', va='bottom', fontweight='bold')\n",
    "\n",
    "# 预测值与实际值散点图（最佳模型）\n",
    "best_model_name = performance_df.iloc[0]['模型']\n",
    "best_y_pred = results[best_model_name]['y_pred']\n",
    "axes[1,1].scatter(y_test, best_y_pred, alpha=0.6, color='purple')\n",
    "axes[1,1].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "axes[1,1].set_xlabel('实际收益', fontsize=12)\n",
    "axes[1,1].set_ylabel('预测收益', fontsize=12)\n",
    "axes[1,1].set_title(f'{best_model_name} - 预测值 vs 实际值\\nR² = {performance_df.iloc[0][\"R²分数\"]:.3f}', fontsize=14)\n",
    "axes[1,1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 7.特征重要性分析\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"特征重要性分析\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "# 线性回归系数分析\n",
    "if '线性回归' in results:\n",
    "    lr_model = results['线性回归']['model']\n",
    "    lr_coefficients = pd.DataFrame({\n",
    "        '特征名称': X.columns,\n",
    "        '系数': lr_model.coef_,\n",
    "        '系数绝对值': abs(lr_model.coef_)\n",
    "    }).sort_values('系数绝对值', ascending=False)\n",
    "    \n",
    "    print(\"线性回归系数分析:\")\n",
    "    print(lr_coefficients)\n",
    "\n",
    "# 随机森林特征重要性\n",
    "if '随机森林' in results:\n",
    "    rf_model = results['随机森林']['model']\n",
    "    rf_importances = pd.DataFrame({\n",
    "        '特征名称': X.columns,\n",
    "        '重要性': rf_model.feature_importances_\n",
    "    }).sort_values('重要性', ascending=False)\n",
    "    \n",
    "    print(\"\\n随机森林特征重要性:\")\n",
    "    print(rf_importances)\n",
    "\n",
    "# 梯度提升树特征重要性\n",
    "if '梯度提升树' in results:\n",
    "    gbdt_model = results['梯度提升树']['model']\n",
    "    gbdt_importances = pd.DataFrame({\n",
    "        '特征名称': X.columns,\n",
    "        '重要性': gbdt_model.feature_importances_\n",
    "    }).sort_values('重要性', ascending=False)\n",
    "    \n",
    "    print(\"\\n梯度提升树特征重要性:\")\n",
    "    print(gbdt_importances)\n",
    "\n",
    "# XGBoost特征重要性\n",
    "if 'XGBoost' in results:\n",
    "    xgb_model = results['XGBoost']['model']\n",
    "    xgb_importances = pd.DataFrame({\n",
    "        '特征名称': X.columns,\n",
    "        '重要性': xgb_model.feature_importances_\n",
    "    }).sort_values('重要性', ascending=False)\n",
    "    \n",
    "    print(\"\\nXGBoost特征重要性:\")\n",
    "    print(xgb_importances)\n",
    "\n",
    "# LightGBM特征重要性\n",
    "if 'LightGBM' in results:\n",
    "    lgb_model = results['LightGBM']['model']\n",
    "    lgb_importances = pd.DataFrame({\n",
    "        '特征名称': X.columns,\n",
    "        '重要性': lgb_model.feature_importances_\n",
    "    }).sort_values('重要性', ascending=False)\n",
    "    \n",
    "    print(\"\\nLightGBM特征重要性:\")\n",
    "    print(lgb_importances)\n",
    "\n",
    "# 8.可视化特征重要性\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "\n",
    "# 线性回归系数\n",
    "if '线性回归' in results:\n",
    "    top_coeff = lr_coefficients.head(10).sort_values('系数', ascending=True)\n",
    "    axes[0,0].barh(top_coeff['特征名称'], top_coeff['系数'], color='steelblue')\n",
    "    axes[0,0].set_xlabel('系数值')\n",
    "    axes[0,0].set_title('线性回归系数分析')\n",
    "    axes[0,0].grid(True, alpha=0.3)\n",
    "else:\n",
    "    axes[0,0].text(0.5, 0.5, '线性回归不可用', \n",
    "                  ha='center', va='center', transform=axes[0,0].transAxes, fontsize=12)\n",
    "    axes[0,0].set_title('线性回归系数分析')\n",
    "\n",
    "# 随机森林特征重要性\n",
    "if '随机森林' in results:\n",
    "    axes[0,1].barh(rf_importances['特征名称'], rf_importances['重要性'], color='forestgreen')\n",
    "    axes[0,1].set_xlabel('特征重要性')\n",
    "    axes[0,1].set_title('随机森林特征重要性')\n",
    "    axes[0,1].grid(True, alpha=0.3)\n",
    "else:\n",
    "    axes[0,1].text(0.5, 0.5, '随机森林不可用', \n",
    "                  ha='center', va='center', transform=axes[0,1].transAxes, fontsize=12)\n",
    "    axes[0,1].set_title('随机森林特征重要性')\n",
    "\n",
    "# 梯度提升树特征重要性\n",
    "if '梯度提升树' in results:\n",
    "    axes[1,0].barh(gbdt_importances['特征名称'], gbdt_importances['重要性'], color='coral')\n",
    "    axes[1,0].set_xlabel('特征重要性')\n",
    "    axes[1,0].set_title('梯度提升树特征重要性')\n",
    "    axes[1,0].grid(True, alpha=0.3)\n",
    "else:\n",
    "    axes[1,0].text(0.5, 0.5, '梯度提升树不可用', \n",
    "                  ha='center', va='center', transform=axes[1,0].transAxes, fontsize=12)\n",
    "    axes[1,0].set_title('梯度提升树特征重要性')\n",
    "\n",
    "# LightGBM特征重要性\n",
    "if 'LightGBM' in results:\n",
    "    axes[1,1].barh(lgb_importances['特征名称'], lgb_importances['重要性'], color='gold')\n",
    "    axes[1,1].set_xlabel('特征重要性')\n",
    "    axes[1,1].set_title('LightGBM特征重要性')\n",
    "    axes[1,1].grid(True, alpha=0.3)\n",
    "else:\n",
    "    axes[1,1].text(0.5, 0.5, 'LightGBM不可用', \n",
    "                  ha='center', va='center', transform=axes[1,1].transAxes, fontsize=12)\n",
    "    axes[1,1].set_title('LightGBM特征重要性')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 9.选择最佳模型并进行详细分析\n",
    "best_model_name = performance_df.iloc[0]['模型']\n",
    "best_model = results[best_model_name]['model']\n",
    "best_r2 = performance_df.iloc[0]['R²分数']\n",
    "\n",
    "print(f\"\\n\" + \"=\"*70)\n",
    "print(f\"最佳模型: {best_model_name}\")\n",
    "print(f\"最佳R²分数: {best_r2:.4f}\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "# 使用最佳模型进行详细预测分析\n",
    "best_y_pred = results[best_model_name]['y_pred']\n",
    "\n",
    "# 10.残差分析\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "# 残差图\n",
    "plt.subplot(1, 3, 1)\n",
    "residuals = y_test - best_y_pred\n",
    "plt.scatter(best_y_pred, residuals, alpha=0.6, color='blue')\n",
    "plt.axhline(y=0, color='red', linestyle='--', linewidth=2)\n",
    "plt.xlabel('预测值')\n",
    "plt.ylabel('残差')\n",
    "plt.title(f'{best_model_name} - 残差图')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 预测分布\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.hist(y_test, alpha=0.7, label='实际值', bins=20, color='blue', edgecolor='black')\n",
    "plt.hist(best_y_pred, alpha=0.7, label='预测值', bins=20, color='red', edgecolor='black')\n",
    "plt.xlabel('收益')\n",
    "plt.ylabel('频数')\n",
    "plt.title('实际值 vs 预测值分布')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 预测误差分布\n",
    "plt.subplot(1, 3, 3)\n",
    "errors = abs(y_test - best_y_pred)\n",
    "plt.hist(errors, bins=20, color='green', alpha=0.7, edgecolor='black')\n",
    "plt.xlabel('绝对误差')\n",
    "plt.ylabel('频数')\n",
    "plt.title('预测误差分布')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 11.详细预测结果对比\n",
    "print(\"\\n前20个样本的详细预测结果:\")\n",
    "comparison_df = pd.DataFrame({\n",
    "    '实际收益': y_test.values[:20],\n",
    "    '预测收益': best_y_pred[:20],\n",
    "    '绝对误差': abs(y_test.values[:20] - best_y_pred[:20]),\n",
    "    '相对误差(%)': (abs(y_test.values[:20] - best_y_pred[:20]) / y_test.values[:20] * 100)\n",
    "})\n",
    "\n",
    "print(comparison_df.round(2))\n",
    "\n",
    "# 12.模型性能统计\n",
    "print(f\"\\n最佳模型({best_model_name})性能统计:\")\n",
    "print(f\"平均绝对误差: {comparison_df['绝对误差'].mean():.2f}\")\n",
    "print(f\"最大绝对误差: {comparison_df['绝对误差'].max():.2f}\")\n",
    "print(f\"平均相对误差: {comparison_df['相对误差(%)'].mean():.2f}%\")\n",
    "\n",
    "# 13.新数据预测示例\n",
    "print(f\"\\n{'='*70}\")\n",
    "print(\"新数据预测示例\")\n",
    "print(f\"{'='*70}\")\n",
    "\n",
    "# 使用最佳模型预测新数据\n",
    "new_data = [[71, 11, 2]]  # 示例数据\n",
    "prediction = best_model.predict(new_data)\n",
    "print(f\"输入特征: {new_data[0]}\")\n",
    "print(f\"预测收益: {prediction[0]:.2f}\")\n",
    "\n",
    "# 14.如果安装了LightGBM，进行参数调优\n",
    "if LGBM_AVAILABLE:\n",
    "    print(\"\\n\" + \"=\"*70)\n",
    "    print(\"LightGBM参数调优\")\n",
    "    print(\"=\"*70)\n",
    "    \n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    \n",
    "    parameters = {\n",
    "        'num_leaves': [15, 31, 62], \n",
    "        'n_estimators': [20, 30, 50, 70], \n",
    "        'learning_rate': [0.1, 0.2, 0.3, 0.4]\n",
    "    }\n",
    "    \n",
    "    lgb_tuned = LGBMRegressor(random_state=123)\n",
    "    grid_search = GridSearchCV(lgb_tuned, parameters, scoring='r2', cv=5, n_jobs=-1)\n",
    "    \n",
    "    grid_search.fit(X_train, y_train)\n",
    "    print(\"最优参数:\", grid_search.best_params_)\n",
    "    \n",
    "    # 使用调优后的参数重新训练\n",
    "    lgb_tuned_model = LGBMRegressor(**grid_search.best_params_, random_state=123)\n",
    "    lgb_tuned_model.fit(X_train, y_train)\n",
    "    \n",
    "    lgb_tuned_r2 = lgb_tuned_model.score(X_test, y_test)\n",
    "    print(f\"调优后LightGBM R²分数: {lgb_tuned_r2:.4f}\")\n",
    "    \n",
    "    # 添加到比较中\n",
    "    tuned_results = {\n",
    "        '模型': 'LightGBM(调优)',\n",
    "        'R²分数': lgb_tuned_r2,\n",
    "        'RMSE': np.sqrt(mean_squared_error(y_test, lgb_tuned_model.predict(X_test))),\n",
    "        'MAE': mean_absolute_error(y_test, lgb_tuned_model.predict(X_test))\n",
    "    }\n",
    "    \n",
    "    final_performance_df = pd.concat([performance_df, pd.DataFrame([tuned_results])], ignore_index=True)\n",
    "    final_performance_df = final_performance_df.sort_values('R²分数', ascending=False)\n",
    "    \n",
    "    print(\"\\n包含调优LightGBM的最终排名:\")\n",
    "    print(final_performance_df)\n",
    "    \n",
    "    best_model_name = final_performance_df.iloc[0]['模型']\n",
    "    best_r2 = final_performance_df.iloc[0]['R²分数']\n",
    "else:\n",
    "    print(\"\\nLightGBM未安装，跳过参数调优\")\n",
    "    final_performance_df = performance_df\n",
    "\n",
    "print(f\"\\n🎯 最终推荐模型: {best_model_name}\")\n",
    "print(f\"📊 最佳R²分数: {best_r2:.4f}\")\n",
    "print(f\"💡 建议: 在广告收益预测场景中，R² > 0.8 通常表示模型具有良好的预测能力\")\n",
    "\n",
    "# 15.业务应用建议\n",
    "print(f\"\\n{'='*70}\")\n",
    "print(\"业务应用建议\")\n",
    "print(f\"{'='*70}\")\n",
    "\n",
    "print(\"\"\"\n",
    "1. 模型选择: 优先选择R²分数高且误差小的模型\n",
    "2. 特征重要性: 关注对收益预测最重要的广告特征\n",
    "3. 预测应用: 可用于广告投放策略优化和收益预估\n",
    "4. 监控更新: 定期重新训练模型以适应数据变化\n",
    "\"\"\")"
   ]
  },
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   "outputs": [],
   "source": []
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